Energy Efficient Communication in Bluetooth 5.3 Using Fine-Tuned Dynamic Subrating Interval Optimization

Abstract

Bluetooth 5.3 features, including connection subrating and low energy (LE) power control, enhance energy efficiency in internet of things (IoT) devices. However, optimizing energy consumption remains challenging in dynamic environments with varying traffic loads. This research presents a fine-tuned dynamic subrating interval optimization (FINE-DIO) approach, supported by an analytical model, to improve energy usage, communication latency, and packet delivery in Bluetooth 5.3 devices. The model evaluates energy consumption across active, idle, sleep, and advertising states, utilizing adaptive connection subrating and LE power control. The optimization process employs iterative fine-tuning, utilizing gradient-based adjustments and performance metrics such as latency and packet delivery ratio (PDR) to achieve the optimal balance between energy efficiency and communication reliability. The simulations confirm the effectiveness of dynamic subrating and periodic advertising optimization across various IoT applications. The results indicate that FINE-DIO achieves up to 15% energy savings, a 10–15% improvement in PDR, and a 20–25% reduction in communication latency, particularly in high-data-rate and interference-heavy environments.